Cascaded Machine Learning Model Based DoS Attacks Detection and Classification in NoC

Abstract

Network-on-Chip (NoC) is becoming an increasingly common System-on-Chip (SoC) fabric architecture since it matches the characteristics of the SoC's shared storage and high-frequency communication. However, due to the rising utilization of NoC, a large number of adversaries are trying to inject Hardware Trojan (HT) into NoC to obtain profits. An increasing variety of NoC HTs is emerging and implemented, resulting in current detection methods becoming invalid. This paper presents a cascaded machine learning model based Denialof-Service (DoS) attack detection and classification approach. An Support Vector Machine (SVM) and a K-Nearest Neighbor (KNN) model were employed in the framework, which has also been validated on our runtime mixed dataset consisting of normal and attacked data extracted from four traffic pattern cases. The proposed framework achieved an expected detection accuracy: more than 85% on detection in average. And outstanding classification results on every attack: 97% on Flooding, and up to 100% on both Routing Loop and Traffic Diversion.